02283nam 22004693a 450 991091729730332120250123131029.097816116899141611689910(CKB)36716442400041(OCoLC)965617645(ScCtBLL)c36e029a-e34b-4889-87bc-ae724f1bd8ac(Perlego)2329872(oapen)doab26621(EXLCZ)993671644240004120250123i20162018 uu engur|||||||||||txtrdacontentcrdamediacrrdacarrierHemispheric Imaginations : North American Fictions of Latin America /Helmbrecht BreinigHanover, NH, USADartmouth College Press2016Hanover, NH :Dartmouth College Press,2016.1 online resourceRe-Mapping the Transnational: A Dartmouth Series in American Studies9781512600766 1512600768 9781611689723 1611689724 What image of Latin America have North American fiction writers created, found, or echoed, and how has the prevailing discourse about the region shaped their work? How have their writings contributed to the discursive construction of our southern neighbors, and how has the literature undermined this construction and added layers of complexity that subvert any approach based on stereotypes? Combining American Studies, Canadian Studies, Latin American Studies, and Cultural Theory, Breinig relies on long scholarly experience to answer these and other questions. 'Hemispheric Imaginations', an ambitious interdisciplinary study of literary representations of Latin America as encounters with the other, is among the most extensive such studies to date. It will appeal to a broad range of scholars of American Studies.Political Science / International RelationsbisacshPolitical sciencePolitical Science / International RelationsPolitical science.Breinig Helmbrecht906878ScCtBLLScCtBLLBOOK9910917297303321Hemispheric imaginations2028561UNINA06544nam 2200745 n 450 991097462620332120251117073725.00-19-773778-11-280-75989-597866107598970-19-974773-310.1093/oso/9780195120257.001.0001(CKB)2430000000004760(SSID)ssj0000303422(PQKBManifestationID)12079689(PQKBTitleCode)TC0000303422(PQKBWorkID)10276229(PQKB)10786728(Au-PeEL)EBL3053642(CaPaEBR)ebr10375148(CaONFJC)MIL75989(OCoLC)922969947(MiAaPQ)EBC3053642(OCoLC)1406787111(StDuBDS)9780197737781(EXLCZ)99243000000000476019980527e20231998 |y |engurcn|||||||||txtccrModern applied biostatistical methods using S-Plus /Steve Selvin1st ed.New York ;Oxford University Press,2023.xiv, 461 p. illMonographs in epidemiology and biostatistics ;v.28.Oxford scholarship onlineIncludes index.Previously issued in print: 1998.0-19-512025-6 Includes bibliographical references and index.Intro -- Contents -- 1. S-language -- In the beginning -- Three data types-and some input conventions -- Reading values into SPLUS -- S-tools-a beginning set -- S-arithmetic -- More S-tools-intermediate set -- S-tools for statistics -- Statistical distributions in SPLUS -- Arrays and tables -- Matrix algebra tools -- Some additional S-tools -- Four S-code examples -- The .Data file -- Addendum: Built-in editors -- Problem set I -- 2. Descriptive Techniques -- Description of descriptive statistics -- Basic statistical measures -- Histogram smoothing-density estimation -- Stem-and-leaf display -- Comparison of groups-t-test -- Comparison of groups-boxplots -- Comparison of data to a theoretical distribution-quantile plots -- Comparison of groups-qqplots -- xy-plot -- Three-dimensional plots-perspective plots -- Three-dimensional plots-contour plots -- Three-dimensional plots-rotation -- Smoothing -- Two-dimensional smoothing of spatial data -- Clusters as a description of data -- Additivity-"sweeping" an array -- Example-geographic calculations using S-functions -- Estimation of the center of a two-dimensional distribution -- Addendum: S-geometry -- Problem set II -- 3. Simulation: Random Values -- Random uniform values -- An example -- Sampling without and with replacement -- Random sample from a discrete probability distribution-acceptance/rejection sampling -- Random sample from a discrete probability distribution-inverse transform method -- Binomial probability distribution -- Hypergeometric probability distribution -- Poisson probability distribution -- Geometric probability distribution -- Random samples from a continuous distribution -- Inverse transform method -- Simulating values from the normal distribution -- Four other statistical distributions -- Simulating minimum and maximum values -- Butler's method.Random values over a complex region -- Multivariate normal variables -- Problem set III -- 4. General Linear Models -- Simplest case-univariate linear regression -- Multivariable case -- Multivariable linear model -- A closer look at residual values -- Predict-pointwise confidence intervals -- Formulas for glm( ) -- Polynomial regression -- Discriminant analysis -- Linear logistic model -- Categorical data-bivariate linear logistic model -- Multivariable data-linear logistic model -- Goodness-of-fit -- Poisson model -- Multivariable Poisson model -- Problem set IV -- 5. Estimation -- Estimation: Maximum Likelihood -- Estimator properties -- Maximum likelihood estimator -- Scoring to find maximum likelihood estimates -- Multiparameter estimation -- Generalized scoring -- Estimation: Bootstrap -- Background -- General outline -- Sample mean from a normal population -- Confidence limits -- An example-relative risk -- Median -- Simple linear regression -- Jackknife estimation -- Bias estimation -- Two-sample test-bootstrap approach -- Two-sample test-randomization approach -- Estimation: Least Squares -- Least squares properties -- Non-linear least squares estimation -- Problem set V -- 6. Analysis of Tabular Data -- Two by two tables -- Matched pairs-binary response -- Two by k table -- Measures of association-2 x 2 table -- Measures of association-r x c table -- Measures of association-table with ordinal variables -- Loglinear model -- Multidimensional-k-level variables -- High dimensional tables -- Problem set VI -- 7. Analysis of Variance and Some Other S-Functions -- Analysis of variance -- One-way design -- Nested design -- Two-way classification with one observation per cell -- Matched pairs-measured response -- Two-way classification with more than one observation per cell -- Leaps-a model selection technique -- Principal components.Canonical correlations -- Problem set VII -- 8. Rates, Life Tables, and Survival -- Rates -- Life tables -- Survival analysis-an introduction -- Nonparametric estimation of a survival curve -- Hazard rate-estimation -- Mean/median survival time -- Proportional hazards model -- Problem set VIII -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.Statistical analysis typically involves applying theoretically generated techniques to the description and interpretation of collected data. This text combines theory, application and interpretation to create an entire biostatistical process.Monographs in epidemiology and biostatistics ;v.28.Oxford scholarship online.BiometryBiologyData processingS (Computer program language)Biometry.BiologyData processing.S (Computer program language)570/.1/5195Selvin S.144781DLCDLCUkStDuBDSZStDuBDSZBOOK9910974626203321Modern applied biostatistical methods using S-Plus4524562UNINA